class_docstr: Main network which does cross-lingual embeddings training
code: def __init__(self, hparams, src_vec, tgt_vec, vocab_size):
      # Hyperparameters
      self.batch_size = hparams.batch_size
      self.smooth_val = hparams.smooth_val
      self.beta = hparams.beta
      self.most_freq = hparams.most_freq
      self.emb_dim = hparams.emb_dim
      self.vocab_size = vocab_size
      self.disc_runs = hparams.disc_runs
      self.iters_epoch = hparams.iters_epoch
      self.src_vec = src_vec
      self.tgt_vec = tgt_vec
      self.src_ten = tf.convert_to_tensor(src_vec)
      self.tgt_ten = tf.convert_to_tensor(tgt_vec)
      self.save_dir = hparams.weight_dir
      self.slang = hparams.src_lang
      self.tlang = hparams.tgt_lang
  
      # Placeholders
      self.lr_ph = tf.placeholder(tf.float32, name="lrPh")
      # Build Graph
      self._build_network_graph()
      self._build_train_graph()
  
imports: from __future__ import print_function, division
  import io
  import os
  import time
  import numpy
  import scipy
  import tensorflow
label_desc: Use image node_id 1 to create a new WordTranslator object  with example: obj = WordTranslator(hparams, src_vec, tgt_vec, vocab_size)